AbstractLearning concise data representations without supervisory signals is a fundamental challenge in machine learning. A prominent approach to this goal is likelihood-based models such as variational autoencoders (VAE) to learn latent representations based on a meta-prior, which is a general premise assumed beneficial for downstream tasks (e.g.,
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